LSTM（二）：Overview

LSTM（二）：Overview

2020/12/04

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https://pixabay.com/zh/photos/photos-album-photographer-old-256889/

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◎ Abstract

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◎ Introduction

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本論文要解決（它之前研究）的（哪些）問題（弱點）？ 

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# RNN。

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◎ Method

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解決方法？ 

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# LSTM。

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具體細節？

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◎ Result

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本論文成果。 

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◎ Discussion

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本論文與其他論文（成果或方法）的比較。 

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成果比較。 

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方法比較。 

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◎ Conclusion 

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◎ Future Work

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後續相關領域的研究。 

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後續延伸領域的研究。

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◎ References

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# RNN。被引用 11946 次。

Elman, Jeffrey L. "Finding structure in time." Cognitive science 14.2 (1990): 179-211.

https://cogsci.ucsd.edu/~rik/courses/readings/elman90-fsit.pdf

# LSTM。被引用 39743 次。

Hochreiter, Sepp, and Jürgen Schmidhuber. "Long short-term memory." Neural computation 9.8 (1997): 1735-1780.

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.676.4320&rep=rep1&type=pdf

# Word2vec 1。被引用 18991 次。

Mikolov, Tomas, et al. "Efficient estimation of word representations in vector space." arXiv preprint arXiv:1301.3781 (2013).

https://arxiv.org/pdf/1301.3781.pdf

# Word2vec 2。被引用 23990 次。

Mikolov, Tomas, et al. "Distributed representations of words and phrases and their compositionality." Advances in neural information processing systems. 2013.

https://papers.nips.cc/paper/2013/file/9aa42b31882ec039965f3c4923ce901b-Paper.pdf

# Word2vec 3。被引用 645 次。

Rong, Xin. "word2vec parameter learning explained." arXiv preprint arXiv:1411.2738 (2014).

https://arxiv.org/pdf/1411.2738.pdf

# C&W v1。被引用 5099 次。

Collobert, Ronan, and Jason Weston. "A unified architecture for natural language processing: Deep neural networks with multitask learning." Proceedings of the 25th international conference on Machine learning. 2008.

http://www.cs.columbia.edu/~smaskey/CS6998-Fall2012/supportmaterial/colbert_dbn_nlp.pdf

# C&W v2。被引用 6841 次。本篇論文闡釋了從 Word2vec 繼續發展 Paragraph2vec 的必要性。

Collobert, Ronan, et al. "Natural language processing (almost) from scratch." Journal of machine learning research 12.ARTICLE (2011): 2493-2537.

https://www.jmlr.org/papers/volume12/collobert11a/collobert11a.pdf

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Understanding LSTM Networks -- colah's blog

http://colah.github.io/posts/2015-08-Understanding-LSTMs/

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